/* step 1 */
void FFNet_propagate (FFNet me, const double input[], double output[])
{
double act; long i, j, k = 1;
long nNodes = my outputsAreLinear ? my nNodes - my nOutputs : my nNodes;
/* clamp input pattern on the network */
for (i=1; i <= my nUnitsInLayer[0]; i++) my activity[i] = input[i];
/* on hidden units use activation function */
for (i=my nUnitsInLayer[0]+2; i <= nNodes; i++)
{
if (my isbias[i]) continue;
for (act=0.0, j=my nodeFirst[i]; j <= my nodeLast[i]; j++, k++)
act += my w[k] * my activity[j];
my activity[i] = my nonLinearity (me, act, & my deriv[i]);
}
/* on output units use another activation function */
if (my outputsAreLinear)
{
for (i=nNodes+1; i <= my nNodes; i++)
{
if (my isbias[i]) continue;
for (act=0.0, j=my nodeFirst[i]; j <= my nodeLast[i]; j++, k++)
act += my w[k] * my activity[j];
my activity[i] = act;
my deriv[i] = 1.0;
}
}
if (output) for (k=my nNodes-my nOutputs+1, i=1; i <= my nOutputs; i++, k++)
output[i] = my activity[k];
}
/* step 1 */
void FFNet_propagate (FFNet me, const double input[], double output[]) {
// clamp input pattern on the network
for (long i = 1; i <= my nUnitsInLayer[0]; i++) {
my activity[i] = input[i];
}
// on hidden units use activation function
long k = 1, nNodes = my outputsAreLinear ? my nNodes - my nOutputs : my nNodes;
for (long i = my nUnitsInLayer[0] + 2; i <= nNodes; i++) {
if (my isbias[i]) {
continue;
}
double act = 0.0;
for (long j = my nodeFirst[i]; j <= my nodeLast[i]; j++, k++) {
act += my w[k] * my activity[j];
}
my activity[i] = my nonLinearity (me, act, & my deriv[i]);
}
// on output units use another activation function
if (my outputsAreLinear) {
for (long i = nNodes + 1; i <= my nNodes; i++) {
if (my isbias[i]) {
continue;
}
double act = 0.0;
for (long j = my nodeFirst[i]; j <= my nodeLast[i]; j++, k++) {
act += my w[k] * my activity[j];
}
my activity[i] = act;
my deriv[i] = 1.0;
}
}
k = my nNodes - my nOutputs + 1;
if (output) {
for (long i = 1; i <= my nOutputs; i++, k++) {
output[i] = my activity[k];
}
}
}
